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Adams J, Khan N, Morris A, Elhabian S. Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. STACOM (WORKSHOP) 2022; 13593:143-156. [PMID: 37103466 PMCID: PMC10122954 DOI: 10.1007/978-3-031-23443-9_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.
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Affiliation(s)
- Jadie Adams
- Scientific Computing and Imaging Institute, University of Utah, UT, USA
- School of Computing, University of Utah, UT, USA
| | - Nawazish Khan
- Scientific Computing and Imaging Institute, University of Utah, UT, USA
- School of Computing, University of Utah, UT, USA
| | - Alan Morris
- Scientific Computing and Imaging Institute, University of Utah, UT, USA
- School of Computing, University of Utah, UT, USA
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, University of Utah, UT, USA
- School of Computing, University of Utah, UT, USA
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Porras AR, Keating RF, Lee JS, Linguraru MG. Predictive Statistical Model of Early Cranial Development. IEEE Trans Biomed Eng 2022; 69:537-546. [PMID: 34324420 PMCID: PMC8776594 DOI: 10.1109/tbme.2021.3100745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We present a data-driven method to build a spatiotemporal statistical shape model predictive of normal cranial growth from birth to the age of 2 years. METHODS The model was constructed using a normative cross-sectional computed tomography image dataset of 278 subjects. First, we propose a new standard representation of the calvaria using spherical maps to establish anatomical correspondences between subjects at the cranial sutures - the main areas of cranial bone expansion. Then, we model the cranial bone shape as a bilinear function of two factors: inter-subject anatomical variability and temporal growth. We estimate these factors using principal component analysis on the spatial and temporal dimensions, using a novel coarse-to-fine temporal multi-resolution approach to mitigate the lack of longitudinal images of the same patient. RESULTS Our model achieved an accuracy of 1.54 ± 1.05 mm predicting development on an independent longitudinal dataset. We also used the model to calculate the cranial volume, cephalic index and cranial bone surface changes during the first two years of age, which were in agreement with clinical observations. SIGNIFICANCE To our knowledge, this is the first data-driven and personalized predictive model of cranial bone shape development during infancy and it can serve as a baseline to study abnormal growth patterns in the population.
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Affiliation(s)
- Antonio R. Porras
- Department of Biostatistics and Informatics at the Colorado School of Public Health and the Department of Pediatrics at the School of Medicine, University of Colorado Anschutz Medical Campus.,Departments of Plastic & Reconstructive Surgery and Neurosurgery at the Children’s Hospital Colorado, Aurora. CO, 80045, USA
| | - Robert F. Keating
- Department of Neurosurgery at the Children’s National Hospital, Washington, DC, 20010, USA
| | - Janice S. Lee
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute of Pediatric Surgical Innovation at Children’s National Hospital, Washington, DC, 20010, USA.,Departments of Radiology and Pediatrics at the George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
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Abstract
The human visual system reliably extracts shape information from complex natural scenes in spite of noise and fragmentation caused by clutter and occlusions. A fast, feedforward sweep through ventral stream involving mechanisms tuned for orientation, curvature, and local Gestalt principles produces partial shape representations sufficient for simpler discriminative tasks. More complete shape representations may involve recurrent processes that integrate local and global cues. While feedforward discriminative deep neural network models currently produce the best predictions of object selectivity in higher areas of the object pathway, a generative model may be required to account for all aspects of shape perception. Research suggests that a successful model will account for our acute sensitivity to four key perceptual dimensions of shape: topology, symmetry, composition, and deformation.
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Affiliation(s)
- James H Elder
- Centre for Vision Research, York University, Toronto, Ontario M3J 1P3, Canada;
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Wilder J, Fruend I, Elder JH. Frequency tuning of shape perception revealed by classification image analysis. J Vis 2018; 18:9. [PMID: 30140891 DOI: 10.1167/18.8.9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Classification image analysis is a powerful technique for elucidating linear detection and discrimination mechanisms, but it has primarily been applied to contrast detection. Here we report a novel classification image methodology for identifying linear mechanisms underlying shape discrimination. Although prior attempts to apply classification image methods to shape perception have been confined to simple radial shapes, the method proposed here can be applied to general 2-D (planar) shapes of arbitrary complexity, including natural shapes. Critical to the method is the projection of each target shape onto a Fourier descriptor (FD) basis set, which allows the essential perceptual features of each shape to be represented by a relatively small number of coefficients. We demonstrate that under this projection natural shapes are low pass, following a relatively steep power law. To efficiently identify the observer's classification template, we employ a yes/no paradigm and match the spectral density of the stimulus noise in FD space to the power law density of the target shape. The proposed method generates linear template models for animal shape detection that are predictive of human judgments. These templates are found to be biased away from the ideal, overly weighting lower frequencies. This low-pass bias suggests that higher frequency shape processing relies on nonlinear mechanisms.
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Affiliation(s)
- John Wilder
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Ingo Fruend
- Centre for Vision Research, York University, Toronto, Canada
| | - James H Elder
- Centre for Vision Research, York University, Toronto, Canada
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A discriminative feature selection approach for shape analysis: Application to fetal brain cortical folding. Med Image Anal 2016; 35:313-326. [PMID: 27498089 DOI: 10.1016/j.media.2016.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 07/08/2016] [Accepted: 07/20/2016] [Indexed: 11/22/2022]
Abstract
The development of post-processing reconstruction techniques has opened new possibilities for the study of in-utero fetal brain MRI data. Recent cortical surface analysis have led to the computation of quantitative maps characterizing brain folding of the developing brain. In this paper, we describe a novel feature selection-based approach that is used to extract the most discriminative and sparse set of features of a given dataset. The proposed method is used to sparsely characterize cortical folding patterns of an in-utero fetal MR dataset, labeled with heterogeneous gestational age ranging from 26 weeks to 34 weeks. The proposed algorithm is validated on a synthetic dataset with both linear and non-linear dynamics, supporting its ability to capture deformation patterns across the dataset within only a few features. Results on the fetal brain dataset show that the temporal process of cortical folding related to brain maturation can be characterized by a very small set of points, located in anatomical regions changing across time. Quantitative measurements of growth against time are extracted from the set selected features to compare multiple brain regions (e.g. lobes and hemispheres) during the considered period of gestation.
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de Souza FD, Sarkar S, Srivastava A, Su J. Pattern theory for representation and inference of semantic structures in videos. Pattern Recognit Lett 2016. [DOI: 10.1016/j.patrec.2016.01.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Kolesov I, Lee J, Sharp G, Vela P, Tannenbaum A. A Stochastic Approach to Diffeomorphic Point Set Registration with Landmark Constraints. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2016; 38:238-51. [PMID: 26761731 PMCID: PMC4727970 DOI: 10.1109/tpami.2015.2448102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
This work presents a deformable point set registration algorithm that seeks an optimal set of radial basis functions to describe the registration. A novel, global optimization approach is introduced composed of simulated annealing with a particle filter based generator function to perform the registration. It is shown how constraints can be incorporated into this framework. A constraint on the deformation is enforced whose role is to ensure physically meaningful fields (i.e., invertible). Further, examples in which landmark constraints serve to guide the registration are shown. Results on 2D and 3D data demonstrate the algorithm's robustness to noise and missing information.
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Affiliation(s)
- Ivan Kolesov
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790
| | - Jehoon Lee
- Samsung Electronics Co., Ltd., Suwon, South Korea
| | - Gregory Sharp
- Department of Radiation Oncology at Massachusetts General Hospital, Boston, MA 02114
| | - Patricio Vela
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics, Stony Brook University, Stony Brook, NY 11790
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Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. Int J Comput Vis 2012; 103:22-59. [PMID: 23956495 DOI: 10.1007/s11263-012-0592-x] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.
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Affiliation(s)
- S Durrleman
- Scientific Computing and Imaging (SCI) Institute, 72 S. Central Drive, Salt Lake City, UT 84112, USA
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Toews M, Wells WM, Zöllei L. A feature-based developmental model of the infant brain in structural MRI. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2012; 15:204-11. [PMID: 23286050 PMCID: PMC4009075 DOI: 10.1007/978-3-642-33418-4_26] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.
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Affiliation(s)
- Matthew Toews
- Brigham and Women's Hospital, Harvard Medical School, USA.
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Diffeomorphic 3D Image Registration via Geodesic Shooting Using an Efficient Adjoint Calculation. Int J Comput Vis 2011. [DOI: 10.1007/s11263-011-0481-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Brun CC, Lepore N, Pennec X, Chou YY, Lee AD, de Zubicaray G, McMahon KL, Wright MJ, Gee JC, Thompson PM. A nonconservative Lagrangian framework for statistical fluid registration-SAFIRA. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:184-202. [PMID: 20813636 DOI: 10.1109/tmi.2010.2067451] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate of strain. In , the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q) . Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.
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Affiliation(s)
- Caroline C Brun
- Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA
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Micheas AC, Wikle CK. A Bayesian Hierarchical Nonoverlapping Random Disc Growth Model. J Am Stat Assoc 2009. [DOI: 10.1198/jasa.2009.0124] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Direct Estimation of Biological Growth Properties from Image Data Using the “GRID” Model. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-02611-9_82] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Lefèvre J, Leroy F, Khan S, Dubois J, Huppi PS, Baillet S, Mangin JF. Identification of Growth Seeds in the Neonate Brain through Surfacic Helmholtz Decomposition. LECTURE NOTES IN COMPUTER SCIENCE 2009; 21:252-63. [DOI: 10.1007/978-3-642-02498-6_21] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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